Multi-armed Bandits in Production at Stitch Fix
Speaker Bio:
Brian Amadio, Data Platform Engineer
Stitch Fix
Brian is a Data Platform Engineer at Stitch Fix, helping to build and maintain the company's innovative experimentation platform. Recently he developed Mab, a production-ready open-source library for multi-armed bandit selection strategies. You can find a live demo of the library at the end of the talk he gave at O'Reilly Superstream: AI & ML in Production. You can also find out more about the Stitch Fix experimentation platform and multi-armed bandits in the blog post he wrote for the Stitch Fix Multithreaded blog.
Previously he worked as a data scientist, delivering high-value Machine Learning projects and solving challenging data problems across a range of complex domains. He has a Ph.D. in experimental particle physics from UC Berkeley, where I analyzed huge datasets from the Large Hadron Collider in search of supersymmetry and microscopic black holes (he never found any).
Abstract:
Multi-armed Bandits in Production at Stitch Fix. Multi-armed bandits have become a popular method for online experimentation which can often out-perform traditional A/B tests. In this talk, Brian Amadio will explain the challenges to scaling multi-armed bandits, and how he solved them for the Stitch Fix experimentation platform. His solution allows Data Scientists to build and integrate sophisticated contextual bandit reward models, and includes an entirely new method for efficient, deterministic Thompson sampling.